Advancements in mobile technology make it possible for advertisers to go beyond real-time static location and contextual information on consumers. In a new study by the Carnegie Mellon University, scientists used a trajectory-based targeting strategy that tracks consumer’s movements like where, when, and for how long they are in a shopping mall, to determine the economic choices.
According to scientists, it can significantly improve advertising via mobile phones.
Beibei Li, assistant professor of information systems and management at Carnegie Mellon University‘s Heinz College of Information Systems and Public Policy, who co-authored the study said, “Our results can help advertisers improve the design and effectiveness of their mobile marketing strategies.”
To analyze the effectiveness of this new strategy, scientists designed a large-scale randomized field experiment in a large shopping mall that involved 83,370 unique user responses for a 14-day period in June 2014. Scientists found that trajectory-based mobile targeting can, as compared with other baselines, lead to higher redemption probability, faster redemption behavior, and higher transaction amounts.
In addition, trajectory-based targeting led to higher customer satisfaction among participants. Trajectory-based mobile targeting also increased total revenues from the stores that were associated with the promotion, as well as overall revenue for the shopping mall. It was less effective in raising overall mall revenues on weekends, and less effective for shoppers who were exploring products across a range of categories instead of considering buying something from just one category.
It is especially effective in influencing high-income consumers.
Anindya Ghose, professor of business at New York University, who co-authored the study said, “The findings suggest that highly targeted mobile promotions can have the inadvertent impact of reducing impulse-purchasing behavior by customers who are in an exploratory shopping stage. On a broader note, our work can be viewed as a first step toward the study of large-scale, fine-grained digital traces of individual physical behavior and how they can be used to predict—and market according to—individuals’ anticipated future behavior.”
The Google and Adobe.